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Light Acquisition

Light Acquisition

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Tea leaf age quality: Age-stratified tea leaf quality classification dataset.

Md Mohsin Kabir¹, Md Sadman Hafiz², Shattik Bandyopadhyaa²

¹Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.

|May 7, 2024

Summary

The "Tea Leaf Age Quality" dataset reveals that younger tea leaves (T1) offer superior quality, while older leaves (T4) are less suitable for brewing. This resource aids machine learning in tea classification and quality prediction.

Keywords:

Deep learning Image Processing in Agriculture Image annotation Machine Learning in Agriculture Quality prediction Tea leaf classification

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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Area of Science:

Agricultural Science
Machine Learning
Data Science

Background:

Tea quality is significantly influenced by leaf age, impacting flavor and chemical composition.
Accurate tea leaf classification and quality assessment are crucial for the agricultural industry.
Existing datasets may lack the detailed age-stratification necessary for advanced machine learning models.

Purpose of the Study:

To introduce the "Tea Leaf Age Quality" dataset, a novel resource for tea leaf classification, detection, and quality prediction.
To provide a comprehensive collection of images systematically categorized by tea leaf age.
To facilitate research in machine learning applications for agricultural product assessment.

Main Methods:

Collected 2208 raw images from three tea gardens in Bangladesh.
Categorized images into four age-based classes: T1 (1-2 days), T2 (3-4 days), T3 (5-7 days), and T4 (7+ days).
Included raw, annotated, and augmented data for diverse research applications.

Main Results:

Younger tea leaves (T1) demonstrated superior quality compared to older leaves (T4).
The dataset captures natural leaf diversity and provides age-stratified categorization.
Established a correlation between tea leaf age and brewing quality.

Conclusions:

The "Tea Leaf Age Quality" dataset is a valuable tool for developing advanced deep learning models in tea quality assessment.
This resource supports technological advancements in the agricultural sector through precise tea leaf categorization.
The findings highlight the importance of leaf age in determining tea quality for optimal brewing.